PERSONNEL

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Feng Yang, PhD

Computational Health Research Branch

Contact Information
Building 38A - Lister Hill Center, 9S909
301.827.1713
feng.yang2@nih.gov


Expertise and Research Interests:

Feng Yang, PhD, joined the Lister Hill National Center for Biomedical Communications (LHNCBC), National Library of Medicine (NLM) in October 2017, is current a Research Fellow in NLM. She is also a visiting Professor in GuiZhou University. Dr. Yang had been working as a Principal Investigator, Associate Professor in Beijing Jiaotong University in China from 2012 to 2019. Dr. Yang received her PhD degree from National Institute of Applied Science (INSA Lyon) in France in 2011, and her B.S. and M.S. degrees from Northwestern Polytechnical University in China in 2005 and 2007, respectively. Her current research interests include machine learning and artificial intelligence based biomedical image processing and analysis. She has so far published more than 60 research papers, including 26 journal articles, 1 book chapters, and 36 conference proceedings.

Honors and Awards:

Dr. Feng Yang received the NLM Special Acts/Services Group Award in 2018.


Publications:

Yang F, Yu H, Kantipudi K, Rosenthal A, Hurt D, Antani S, Yaniv ZR, Jaeger S. Differentiating between Drug-Sensitive and Drug-Resistant Tuberculosis with Machine Learning for Clinical and Radiological Features. Quantitative Imaging in Medicine and Surgery, 0(0): 1–16, 2021. Publish Ahead of Print.

Niu P, Wang L, Xie B, Robini M, Boussel, L Douek P, Zhu Y, Yang F. Improved Image Reconstruction Using Multi-Energy Information in Spectral Photon-Counting CT. IEEE Access, vol. 9, pp. 97981-97989, 2021, doi: 10.1109/ACCESS.2021.3083505.

Karki M, Kantipudi K, Yu H, Yang F, Kassim Y, Yaniv Z,Jaeger S. Identifying Drug-Resistant Tuberculosis in Chest Radiographs: Evaluation of CNN Architectures and Training Strategies. 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, accepted on July 15th, 2021, will be held virtually October 31 – November 4, 2021.

Kassim YM, Palaniappan K, Yang F, Poostchi M, Palaniappan N, Maude RJ, Antani S, Jaeger S. Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears. IEEE J Biomed Health Inform. 2021 May;25(5):1735-1746. doi: 10.1109/JBHI.2020.3034863. Epub 2021 May 11.

Robini MC, Yang F, Zhu Y. A stochastic approach to full inverse treatment planning for charged-particle therapy. J Glob Optim 77, 853–893 (2020). https://doi.org/10.1007/s10898-020-00902-2

Li Z, Wang X, Wang L, Ji W, Zhang M, Zhu Y, Yang F. UNet-ESPC-Cascaded Super-Resolution Reconstruction in Spectral CT. 2020 15th IEEE International Conference on Signal Processing (ICSP).

Yu H, Yang F, Rajaraman S, Ersoy I, Moallem G, Poostchi M, Palaniappan K, Antani S, Maude RJ, Jaeger S. Malaria Screener: a smartphone application for automated malaria screening. BMC Infect Dis. 2020 Nov 11;20(1):825. doi: 10.1186/s12879-020-05453-1.

Yang F, Quizon N, Silamut K, Maude RJ, Jaeger S, Antani SK. Cascading YOLO: Automated Malaria Parasite Detection for Plasmodium Vivax in Thin Blood Smears. Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141Q (16 March 2020); https://doi.org/10.1117/12.2549701

Yu H, Yang F, Silamut R, Maude S, Jaeger S, Antani SK. Automatic Blood Smear Analysis with Artificial Intelligence and Smartphones. ASTMH 68th Annual Meeting, Washington DC, Nov. 20-24, 2019.

Yang F, Poostchi M, Silamut K, Maude RJ, Jaeger S, Thoma G. Automated Parasite Classification of Malaria on Thick Blood Smears. ASTMH 67th Annual Meeting, New Orleans, LA, Oct. 28 – Nov. 1, 2018.

Yang F, Yu H, Silamut K, Maude RJ, Jaeger S, Antani SK. Parasite Detection in Thick Blood Smears Based on Customized Faster-RCNN. Proceedings of AIPR2019, Washington DC, USA, Oct 15-17, 2019.

Yang F, Yu H, Silamut K, Maude R, Jaeger S, Antani SK. Smartphone-Supported Malaria Diagnosis Based on Deep Learning. Proceedings of 10th Workshop on Machine Learning in Medical Imaging (MLMI 2019) in conjunction with MICCAI, Shenzhen, China, Oct 13-17, 2019.

Yang F, Poostchi M, Yu H, Zhou Z, Silamut K, Yu J, Maude RJ, Jaeger S, Antani S. . Deep learning for smartphone-based malaria parasite detection in thick blood smears. IEEE J Biomed Health Inform. 2020 May;24(5):1427-1438. doi: 10.1109/JBHI.2019.2939121. Epub 2019 Sep 23.

Jaeger S, Juarez-Espinosa OH, Candemir S, Poostchi M, Yang F, Kim L, Ding M, Folio LR, Antani SK, Gabrielian A, Hurt D, Rosenthal A, Thoma GR. Detecting drug-resistant tuberculosis in chest radiographs. Int J Comput Assist Radiol Surg. 2018 Dec;13(12):1915-1925. doi: 10.1007/s11548-018-1857-9. Epub 2018 Oct 3.

Yang F, Yu H, Poostchi M, Silamut K, Maude RJ, Jaeger S. Smartphone-Supported Automated Malaria Parasite Detection. SIIM conference on Machine Intelligence in Medical Imaging, 2018.

Jaeger S, Antani SK, Rajaraman S, Yang F, Yu H. Malaria Screening: Research into Image Analysis and Deep Learning. Report to the Board of Scientific Counselors September 2018.